# WaDec: Decompiling WebAssembly Using Large Language Model

arXiv: https://arxiv.org/abs/2406.11346 · HTML: https://mlsanity.com/paper/2406.11346/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2024.

## Built on by (most cited first, 5 of 5)

- [Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets](https://mlsanity.com/paper/2503.17502/) (2025)
- [Towards LLM-based optimization compilers. Can LLMs learn how to apply a single peephole optimization? Reasoning is all LLMs need!](https://mlsanity.com/paper/2412.12163/) (2024)
- [Challenges and Future Directions in Agentic Reverse Engineering Systems](https://mlsanity.com/paper/2604.14317/) (2026)
- [AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning](https://mlsanity.com/paper/2606.20373/) (2026)
- [NeuroDeX: Unlocking Diverse Support in Decompiling Deep Neural Network Executables](https://mlsanity.com/paper/2509.06402/) (2025)

## Abstract (arXiv)

WebAssembly (abbreviated Wasm) has emerged as a cornerstone of web development, offering a compact binary format that allows high-performance applications to run at near-native speeds in web browsers. Despite its advantages, Wasm's binary nature presents significant challenges for developers and researchers, particularly regarding readability when debugging or analyzing web applications. Therefore, effective decompilation becomes crucial. Unfortunately, traditional decompilers often struggle with producing readable outputs. While some large language model (LLM)-based decompilers have shown good compatibility with general binary files, they still face specific challenges when dealing with Wasm. In this paper, we introduce a novel approach, WaDec, which is the first use of a fine-tuned LLM to interpret and decompile Wasm binary code into a higher-level, more comprehensible source code representation. The LLM was meticulously fine-tuned using a specialized dataset of wat-c code snippets, employing self-supervised learning techniques. This enables WaDec to effectively decompile not only complete wat functions but also finer-grained wat code snippets. Our experiments demonstrate that WaDec markedly outperforms current state-of-the-art tools, offering substantial improvements across several metrics. It achieves a code inflation rate of only 3.34%, a dramatic 97% reduction compared to the state-of-the-art's 116.94%. Unlike baselines' output that cannot be directly compiled or executed, WaDec maintains a recompilability rate of 52.11%, a re-execution rate of 43.55%, and an output consistency of 27.15%. Additionally, it significantly exceeds state-of-the-art performance in AST edit distance similarity by 185%, cyclomatic complexity by 8%, and cosine similarity by 41%, achieving an average code similarity above 50%.